{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "YC4R6nkCp91d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YC4R6nkCp91d",
    "outputId": "1792fab3-0a0c-48c1-c3ce-f07091a06d3e",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-03-22 17:55:19--  https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 2620:100:601a:18::a27d:712, 162.125.7.18\n",
      "Connecting to www.dropbox.com (www.dropbox.com)|2620:100:601a:18::a27d:712|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: /s/dl/948jr9cfs7fgj99/UBER.zip [following]\n",
      "--2023-03-22 17:55:19--  https://www.dropbox.com/s/dl/948jr9cfs7fgj99/UBER.zip\n",
      "Reusing existing connection to [www.dropbox.com]:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://ucc12f480a811ad66fa4e0167407.dl.dropboxusercontent.com/cd/0/get/B4yVmJruA6tAshAnTEubKx8G6wnJQMzejIhVFEhMm4NAqK5FvHi4ldO2CMkXLZKnBCAYvuMkq6GkyS9_Tzv3WtbtjomXqcMZaFMj-2AV2uLdr9JsgVxCol8hMCCLn1DvOn7A8-HQ2Do0-kdACrhGK8TaZeTdis1ksKUstwraaf1e-w/file?dl=1# [following]\n",
      "--2023-03-22 17:55:19--  https://ucc12f480a811ad66fa4e0167407.dl.dropboxusercontent.com/cd/0/get/B4yVmJruA6tAshAnTEubKx8G6wnJQMzejIhVFEhMm4NAqK5FvHi4ldO2CMkXLZKnBCAYvuMkq6GkyS9_Tzv3WtbtjomXqcMZaFMj-2AV2uLdr9JsgVxCol8hMCCLn1DvOn7A8-HQ2Do0-kdACrhGK8TaZeTdis1ksKUstwraaf1e-w/file?dl=1\n",
      "Resolving ucc12f480a811ad66fa4e0167407.dl.dropboxusercontent.com (ucc12f480a811ad66fa4e0167407.dl.dropboxusercontent.com)... 2620:100:601a:15::a27d:70f, 162.125.7.15\n",
      "Connecting to ucc12f480a811ad66fa4e0167407.dl.dropboxusercontent.com (ucc12f480a811ad66fa4e0167407.dl.dropboxusercontent.com)|2620:100:601a:15::a27d:70f|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1820227 (1.7M) [application/binary]\n",
      "Saving to: ‘data/UBER.zip’\n",
      "\n",
      "data/UBER.zip       100%[===================>]   1.74M  --.-KB/s    in 0.1s    \n",
      "\n",
      "2023-03-22 17:55:20 (14.3 MB/s) - ‘data/UBER.zip’ saved [1820227/1820227]\n",
      "\n",
      "Archive:  data/UBER.zip\n",
      "   creating: data/UBER/\n",
      "  inflating: data/UBER/UBER_2021.html  \n",
      "  inflating: data/__MACOSX/UBER/._UBER_2021.html  \n",
      "  inflating: data/UBER/UBER_2020.html  \n",
      "  inflating: data/__MACOSX/UBER/._UBER_2020.html  \n",
      "  inflating: data/UBER/UBER_2019.html  \n",
      "  inflating: data/__MACOSX/UBER/._UBER_2019.html  \n",
      "  inflating: data/UBER/UBER_2022.html  \n",
      "  inflating: data/__MACOSX/UBER/._UBER_2022.html  \n"
     ]
    }
   ],
   "source": [
    "# download files\n",
    "!mkdir data\n",
    "!wget \"https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\" -O data/UBER.zip\n",
    "!unzip data/UBER.zip -d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1df01cb-ad50-44b8-9f36-5a5788b498df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61",
   "metadata": {
    "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61"
   },
   "outputs": [],
   "source": [
    "from gpt_index import download_loader, GPTSimpleVectorIndex\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
   "metadata": {
    "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
    "tags": []
   },
   "source": [
    "### Ingest Unstructured Data Through the Unstructured.io Reader\n",
    "\n",
    "Leverage the capabilities of Unstructured.io HTML parsing.\n",
    "Downloaded through LlamaHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61fed4f1-8cca-4d98-b916-a3184279e256",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "years = [2022, 2021, 2020, 2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3",
   "metadata": {
    "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3"
   },
   "outputs": [],
   "source": [
    "UnstructuredReader = download_loader(\"UnstructuredReader\", refresh_cache=True, use_gpt_index_import=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29084a11-f3da-428d-ae17-27b85a365e84",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "29084a11-f3da-428d-ae17-27b85a365e84",
    "outputId": "819e4c48-0bb0-4e22-a84c-3021fbedbc4a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /Users/jerryliu/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /Users/jerryliu/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "INFO:unstructured:Reading document from string ...\n",
      "INFO:unstructured:Reading document ...\n",
      "INFO:unstructured:Reading document from string ...\n",
      "INFO:unstructured:Reading document ...\n",
      "INFO:unstructured:Reading document from string ...\n",
      "INFO:unstructured:Reading document ...\n",
      "INFO:unstructured:Reading document from string ...\n",
      "INFO:unstructured:Reading document ...\n"
     ]
    }
   ],
   "source": [
    "loader = UnstructuredReader()\n",
    "doc_set = {}\n",
    "all_docs = []\n",
    "for year in years:\n",
    "    year_docs = loader.load_data(file=Path(f'./data/UBER/UBER_{year}.html'), split_documents=False)\n",
    "    # insert year metadata into each year\n",
    "    for d in year_docs:\n",
    "        d.extra_info = {\"year\": year}\n",
    "    doc_set[year] = year_docs\n",
    "    all_docs.extend(year_docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d",
   "metadata": {
    "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d"
   },
   "source": [
    "### Setup a Vector Index for each SEC filing\n",
    "\n",
    "We setup a separate vector index for each SEC filing from 2019-2022.\n",
    "\n",
    "We also optionally initialize a \"global\" index by dumping all files into the vector store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
   "metadata": {
    "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
    "outputId": "bdc1d207-efb2-4f82-bf6c-668cfb7ca98f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 156882 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 162641 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 173288 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 166363 tokens\n"
     ]
    }
   ],
   "source": [
    "# initialize simple vector indices + global vector index\n",
    "# NOTE: don't run this cell if the indices are already loaded! \n",
    "index_set = {}\n",
    "for year in years:\n",
    "    cur_index = GPTSimpleVectorIndex(doc_set[year], chunk_size_limit=512)\n",
    "    index_set[year] = cur_index\n",
    "    cur_index.save_to_disk(f'index_{year}.json')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34",
   "metadata": {
    "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34"
   },
   "outputs": [],
   "source": [
    "# Load indices from disk\n",
    "index_set = {}\n",
    "for year in years:\n",
    "    cur_index = GPTSimpleVectorIndex.load_from_disk(f'index_{year}.json')\n",
    "    index_set[year] = cur_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007a99d7-4f63-44bf-957b-c9f5669bce28",
   "metadata": {
    "id": "007a99d7-4f63-44bf-957b-c9f5669bce28"
   },
   "source": [
    "### Composing a Graph to synthesize answers across 10-K filings (2019-2022)\n",
    "\n",
    "We want our queries to aggregate/synthesize information across *all* 10-K filings. To do this, we define a List index\n",
    "on top of the 4 vector indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6",
   "metadata": {
    "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6"
   },
   "outputs": [],
   "source": [
    "from gpt_index import GPTListIndex, LLMPredictor\n",
    "from langchain import OpenAI\n",
    "from gpt_index.composability import ComposableGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "966388cb-2fe4-4427-ad2e-cea6a0375233",
   "metadata": {
    "id": "966388cb-2fe4-4427-ad2e-cea6a0375233"
   },
   "outputs": [],
   "source": [
    "# set summary text for each doc\n",
    "for year in years:\n",
    "    index_set[year].set_text(f\"UBER 10-k Filing for {year} fiscal year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22",
   "metadata": {
    "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22"
   },
   "outputs": [],
   "source": [
    "# set number of output tokens\n",
    "llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, max_tokens=512))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
   "metadata": {
    "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
    "outputId": "e7c99f46-8757-48a2-c84b-cb95a0a3eab9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
      "INFO:gpt_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
     ]
    }
   ],
   "source": [
    "# define a list index over the vector indices\n",
    "# allows us to synthesize information across each index\n",
    "list_index = GPTListIndex([index_set[y] for y in years], llm_predictor=llm_predictor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ad798b8-9b91-47a1-adce-848ee35183d3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "3ad798b8-9b91-47a1-adce-848ee35183d3",
    "outputId": "e6043cb8-5aa0-4818-b1a9-69b1e32f96ec"
   },
   "outputs": [],
   "source": [
    "graph = ComposableGraph.build_from_index(list_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "NLtnE--Mya7P",
   "metadata": {
    "id": "NLtnE--Mya7P"
   },
   "outputs": [],
   "source": [
    "graph.save_to_disk('10k_graph.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b92a0ba3-5c6b-485f-8f8d-5e164689ff05",
   "metadata": {
    "id": "b92a0ba3-5c6b-485f-8f8d-5e164689ff05"
   },
   "outputs": [],
   "source": [
    "graph = ComposableGraph.load_from_disk('10k_graph.json', llm_predictor=llm_predictor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1b8410c-2945-4839-8253-31fdbcf36fc1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TMP: define prompt helper\n",
    "from gpt_index import PromptHelper\n",
    "query_prompt_helper = PromptHelper(4096, 256, 0)\n",
    "\n",
    "# define query configs for graph \n",
    "query_configs = [\n",
    "    {\n",
    "        \"index_struct_type\": \"simple_dict\",\n",
    "        \"query_mode\": \"default\",\n",
    "        \"query_kwargs\": {\n",
    "            \"similarity_top_k\": 1,\n",
    "            # \"include_summary\": True,\n",
    "            \"response_mode\": \"tree_summarize\",\n",
    "            \"prompt_helper\": query_prompt_helper\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        \"index_struct_type\": \"list\",\n",
    "        \"query_mode\": \"default\",\n",
    "        \"query_kwargs\": {\n",
    "            \"response_mode\": \"tree_summarize\",\n",
    "            \"verbose\": True,\n",
    "            \"prompt_helper\": query_prompt_helper\n",
    "        }\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f23ba33-416f-433a-9e83-5a37ed060344",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TODO: add aquery to graph\n",
    "import asyncio\n",
    "import time\n",
    "\n",
    "cross_query_str = (\n",
    "    \"Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points.\"\n",
    ")\n",
    "\n",
    "start_time = time.perf_counter()\n",
    "task = graph.aquery(cross_query_str, query_configs=query_configs)\n",
    "response = asyncio.run(task)\n",
    "elapsed_time = time.perf_counter() - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "716c9452-ef9f-4105-a4ad-5d72ad42f94e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "• 2019: Potential legal proceedings, competitive nature of the industry, potential for regulatory changes\n",
      "• 2020: Potential legal proceedings, competitive nature of the industry, potential for regulatory changes, impact of the COVID-19 pandemic\n",
      "• 2021: Risk of Drivers being classified as employees, workers or quasi-employees instead of independent contractors, high competition in mobility, delivery, and logistics industries, risk of COVID-19 pandemic\n",
      "• 2022: Risk of Drivers being classified as employees, workers or quasi-employees instead of independent contractors, competitive landscape of the mobility, delivery, and logistics industries, risk of having to lower fares or service fees, or offer significant Driver incentives and consumer discounts and promotions in order to remain competitive in certain markets, risk of incurring significant losses\n",
      "\n",
      "• 2019-2020: Potential legal proceedings, competitive nature of the industry, potential for regulatory changes\n",
      "• 2021-2022: Risk of Drivers being classified as employees, workers or quasi-employees instead of independent contractors, increased competition in mobility, delivery, and logistics industries, risk of having to lower fares or service fees, or offer significant Driver incentives and consumer discounts and promotions in order to remain competitive in certain markets, risk of incurring significant losses, risk of COVID-19 pandemic\n",
      "21.1803678330034\n"
     ]
    }
   ],
   "source": [
    "print(str(response))\n",
    "print(str(elapsed_time))"
   ]
  }
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